Imaging and video capabilities have become the trend in consumer electronics. Digital cameras, digital camcorders, and video cellular phones are common, and many other new gadgets are evolving in the market. Advances in large resolution CCD/CMOS sensors coupled with the availability of low-power digital signal processors (DSPs) has led to the development of digital cameras with both high resolution image and short audio/visual clip capabilities. The high resolution (e.g., sensor with a 2560×1920 pixel array) provides quality offered by traditional film cameras.
As the camera sensor and signal processing technologies advanced, the nominal performance indicators of camera performance, e.g., picture size, zooming, and range, reached saturation in the market. Then, end users shifted their focus back to actual or perceivable picture quality. The criteria of users in judging picture quality include signal to noise ratio (SNR) (especially in dark regions), blur due to hand shake, blur due to fast moving objects, natural tone, natural color, etc.
The perceived quality of still images and video is heavily influenced by how brightness/contrast of a scene is rendered, which makes brightness/contrast enhancement (BCE) one of the fundamental parts of an image pipeline. BCE is a challenging problem because human perception of brightness/contrast is quite complex and is highly dependent on the content of a still image or video frames. Many current BCE methods do not adequately address this complexity. When tested on large sets of images, these methods may fail in certain scenes (e.g., flat objects, clouds in a sky) because image content is very diverse. That is, many current BCE methods apply a fixed technique to all images/frames regardless of content and, as a result, may produce poor quality results on some images/frames because they do not adapt to content variation. Accordingly, improvements in BCE techniques are desirable.